论文标题
具有分层潜在变量神经过程的双随机变异推断
Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables
论文作者
论文摘要
神经过程（NP）构成了一个随机过程的变异近似模型家族，具有有希望的计算效率和不确定性定量的特性。这些过程使用具有潜在变量输入的神经网络来诱导预测分布。但是，香草NP的表现力受到限制，因为它们仅使用全局潜在变量，而目标特定局部变化有时可能至关重要。为了应对这一挑战，我们系统地研究了NP，并提出了NP模型的新变体，我们称之为双随机变化神经过程（DSVNP）。该模型结合了用于预测的全局潜在变量和局部潜在变量。我们在几个实验中评估了该模型，我们的结果证明了在多输出回归和分类中的不确定性估计中的竞争性预测性能。
Neural processes (NPs) constitute a family of variational approximate models for stochastic processes with promising properties in computational efficiency and uncertainty quantification. These processes use neural networks with latent variable inputs to induce predictive distributions. However, the expressiveness of vanilla NPs is limited as they only use a global latent variable, while target specific local variation may be crucial sometimes. To address this challenge, we investigate NPs systematically and present a new variant of NP model that we call Doubly Stochastic Variational Neural Process (DSVNP). This model combines the global latent variable and local latent variables for prediction. We evaluate this model in several experiments, and our results demonstrate competitive prediction performance in multi-output regression and uncertainty estimation in classification.
